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also use MACD indicator; minor changes

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gcarq
2017-06-05 21:17:10 +02:00
parent 0f5e00618b
commit 029072c9af
4 changed files with 50 additions and 34 deletions
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50
analyze.py
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@@ -1,10 +1,7 @@
import time
from datetime import timedelta
import arrow
import matplotlib
import logging
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
import requests
from pandas.io.json import json_normalize
from stockstats import StockDataFrame
@@ -68,7 +65,8 @@ def populate_trends(dataframe):
] = 1
"""
dataframe.loc[
dataframe['stochrsi'] < 0.20,
(dataframe['stochrsi'] < 0.20)
& (dataframe['macd'] > dataframe['macds']),
'underpriced'
] = 1
dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
@@ -103,27 +101,32 @@ def plot_dataframe(dataframe, pair):
:return: None
"""
import matplotlib
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
# Three subplots sharing x axe
f, (ax1, ax2) = plt.subplots(2, sharex=True)
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
f.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
ax1.plot(dataframe.index.values, dataframe['close_60_ema'], label='EMA(60)')
ax1.plot(dataframe.index.values, dataframe['close_120_ema'], label='EMA(120)')
ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(60)')
ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(120)')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
ax1.legend()
#ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
#ax2.plot(dataframe.index.values, dataframe['macds'], label='MACDS')
#ax2.plot(dataframe.index.values, dataframe['macdh'], label='MACD Histogram')
#ax2.plot(dataframe.index.values, [0] * len(dataframe.index.values))
#ax2.legend()
ax2.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI')
ax2.plot(dataframe.index.values, [0.80] * len(dataframe.index.values))
ax2.plot(dataframe.index.values, [0.20] * len(dataframe.index.values))
ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
ax2.plot(dataframe.index.values, dataframe['macds'], label='MACDS')
ax2.plot(dataframe.index.values, dataframe['macdh'], label='MACD Histogram')
ax2.plot(dataframe.index.values, [0] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI')
ax3.plot(dataframe.index.values, [0.80] * len(dataframe.index.values))
ax3.plot(dataframe.index.values, [0.20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
f.subplots_adjust(hspace=0)
@@ -134,10 +137,9 @@ def plot_dataframe(dataframe, pair):
if __name__ == '__main__':
while True:
pair = 'BTC_ANT'
for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
get_buy_signal(pair)
#dataframe = get_ticker_dataframe(pair)
#dataframe = populate_trends(dataframe)
#plot_dataframe(dataframe, pair)
#time.sleep(60)
#for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
# get_buy_signal(pair)
dataframe = get_ticker_dataframe(pair)
dataframe = populate_trends(dataframe)
plot_dataframe(dataframe, pair)
time.sleep(60)